Wireless Communications and Mobile Computing / 2019 / Article / Psdc 1

Research Article

Backtracking Search Optimization Paradigm for Pattern Correction of Faulty Antenna Array in Wireless Mobile Communications

Pseudocode 1

Pseudocode of BSA with procedural details.
Step  1:Initialization:
An initial population P is initialized as
Where , and , V is the uniform
distribution function, N is population size, and D is problem
dimension. represents chromosome in the population P.
and represents the lower limit upper limit of the solution respectively.
Step  2: Selection-I:
In Selection-I, historical population is determined,
which is used to calculate the direction of search for optimum
solution. Initial is determined by:
.
This process is done through the if-then rule as:
Here is defined as update operation, c and d are random
numbers in the range which help
decide if the Phis is selected from the former generation.
After this step the order of the chromosomes is shuffled by
random shuffling function given by:
Step  3: Mutation:
A mutation is done in to generate a trail population using:
So, is due to the propagation of chromosomes of in the direction
set by ( - ) and G controls amplitude of ( - ). Use of gives a
partial advantage to BSA of the experiences of previous
population in creating an intelligent trial population .
Step  4: Crossover:
It creates the final trial population , the
initial step towards was set by . Target population is improved
by chromosomes of having improved fitness. For this crossover uses two
procedures. First determines a binary matrix map of order. Its
function is to indicate particles of that are to be changed by using
relevant particles of . The initial value of is set as 1,
and trial population is updated as:
Here   &  . Crossover strategy of
BSA is presented in Algorithm 2. BSA uses a unique crossover strategy as
compared to other EAs. The mix-rate parameter controls the number of
particles that are mutated in atrial by using , is mentioned
in line of Algorithm 2. This function is the main reason that BSA crossover
is unique to other EAs
BSA’s is defined by two predefined strategies being used
randomly. The first strategy implements mix-rate (Algorithm 2
line -), and other allows one chromosome to mutate, chosen
at random in each trial (Algorithm 2 line -).
The mutation strategy results in overflow of some particles of
the PT during crossover procedure. For this, a boundary control
process is defined that keeps the individual inside the bounds.
Its algorithm is given in Algorithm 3.
Step  5: Selection-II:
In this process the particles of having improved fitness are
updated by their corresponding better particles of . The global
minimum and global minimizer are also updated based on the best
fitness so far. The particle having best fitness is called global
minimizer denoted by Pbest and its fitness is called global minimum.
Step  6: Termination
Finally, optimization process of the BSA is stopped if one the
following criterion is met:
(i) Max number of iteration is reached, or
(ii) The fitness value is below a certain threshold

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